A Generative Model for Inverse Design of Metamaterials

TL;DR

This paper introduces a deep learning-based generative model that enables inverse design of 2D metamaterials, significantly accelerating the creation of structures with desired optical properties without relying solely on iterative electromagnetic simulations.

Contribution

The work presents a novel deep learning architecture for inverse design of metamaterials, replacing traditional intuition-guided methods with a systematic, data-driven approach.

Findings

Generated patterns achieve about 0.9 spectral accuracy.

The method accelerates metamaterial design process.

High-fidelity optical spectra matching.

Abstract

The advent of two-dimensional metamaterials in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The effective parameters of these architected materials render unprecedented control over the optical properties of light, thereby eliciting previously unattainable applications in flat lenses, holographic imaging, and emission control among others. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell's equations until a locally optimized solution can be attained. In this work, we identify a solution to circumvent this intuition-guided design by means of a deep learning architecture. When fed an input set of optical spectra, the constructed generative network assimilates a candidate pattern from a user-defined dataset of geometric structures in order to match the input spectra. The generated metamaterial patterns demonstrate high fidelity, yielding equivalent optical spectra at an average accuracy of about 0.9. This approach reveals an opportunity to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.